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name: model-apis description: "Use OpenFold programmatic model APIs, config presets, weight imports, protein outputs, validation metrics, and acceleration internals." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

OpenFold Model APIs

Use this sub-skill when a task needs OpenFold from Python: selecting model_config presets, constructing AlphaFold, preparing low-level forward inputs, importing or converting weights, writing PDB/ModelCIF outputs, computing validation metrics, reasoning about optional acceleration flags, or choosing focused maintainer tests.

Route Quickly

• Read references/api-reference.md for source-backed Python entry points, model input/output caveats, structure-output helpers, validation metrics, and safe maintainer test selection.
• Read references/config-presets.md before choosing presets or combining precision, long-sequence, TensorRT, DeepSpeed, cuEquivariance, FlashAttention, or train-mode overrides.
• Read references/weights-and-checkpoints.md to classify JAX .npz, OpenFold .pt/.ckpt, older OpenFold v1, DeepSpeed checkpoint trees, and OpenFold-to-JAX conversion requests.
• Read references/acceleration.md for API-level acceleration switches, optional backend fallback, and hardware-sensitive test guidance.
• Read references/troubleshooting.md when imports, config validation, checkpoint keys/shapes, batch tensors, output conversion, metrics, or optional kernels fail.

Bundled Safe Helpers

• Run scripts/inspect_openfold_api.py --json to inspect package version, import status, safe signatures, preset summaries, parser/config/protein availability, and optional backend availability without constructing a model.
• Run scripts/validate_config_preset.py --preset model_1_ptm --json to validate a preset and selected high-level overrides without instantiating AlphaFold, loading weights, compiling TensorRT engines, or running inference/training.
• Add --package-root /path/to/openfold-or-checkout-root to either helper when the OpenFold package is not installed but an explicit source/package root is available.

Current Verification Caveat

OpenFold package metadata observed during skill creation reported version 2.2.0. The current inspection environment could import package metadata, openfold.config, parser modules, and openfold.np.protein; imports that traverse model/CLI internals failed because attn_core_inplace_cuda was not importable. Treat the model, script-utils, and compiled-kernel sections here as source-backed API guidance until the compiled extension path is repaired and live imports are re-run.

Boundaries

• Route public inference command construction, prediction outputs, relaxation CLI use, and user-facing run_pretrained_openfold.py flags to ../inference/.
• Route training launchers, fine-tuning configs, DeepSpeed launcher setup, and training checkpoint resume behavior to ../training/.
• Route FASTA/MSA/mmCIF parsing, feature layout creation, alignment databases, template caches, and data pipeline details to ../data-preparation/.
• Route installation of PyTorch/CUDA/OpenMM/DeepSpeed/TensorRT/cuEquivariance/FlashAttention and compiled-extension repair to ../installation-assets/.

Minimal Workflow

1. Validate the intended preset and overrides with scripts/validate_config_preset.py before constructing AlphaFold.
2. Build low-level Python integrations only from processed feature tensors; do not pass raw FASTA, MSA, or mmCIF data directly to AlphaFold.forward.
3. Match checkpoint family to preset family before importing weights or planning conversion.
4. Convert prediction outputs with openfold.np.protein helpers and choose ModelCIF when PDB chain limits or metadata make PDB unsuitable.
5. Select native tests only after the runtime environment advertises the needed compiled kernels, optional backends, and safe fixtures.